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eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Resource Allocation for Secure Communications in Cooperat...
Ding Xu, Qun Li · 2018-09-29 · via eess.SP updates on arXiv.org

We consider a cognitive wireless powered communication network (CWPCN) sharing the spectrum with a primary network who faces security threats from eavesdroppers (EAVs). We propose a new cooperative protocol for the wireless powered secondary users (SU) to cooperate with the primary user (PU). In the protocol, the SUs first harvest energy from the power signals transmitted by the cognitive hybrid access point during the wireless power transfer (WPT) phase, and then use the harvested energy to interfere with the EAVs and gain transmission opportunities at the same time during the wireless information transfer (WIT) phase. Taking the maximization of the SU ergodic rate as the design objective, resource allocation algorithms based on the dual optimization method and the block coordinate descent method are proposed for the cases of perfect channel state information (CSI) and collusive/non-collusive EAVs under the PU secrecy constraint. More PU favorable greedy algorithms aimed at minimizing the PU secrecy outage probability are also proposed. We furthermore consider the unknown EAVs' CSI case and propose an efficient algorithm to improve the PU security performance. Extensive simulations show that our proposed protocol and corresponding resource allocation algorithms can not only let the SU gain transmission opportunities but also improve the PU security performance even with unknown EAVs' CSI.